Palm print image matching techniques

ABSTRACT

In some implementations, a method may be used for matching palm print images. A search template that identifies at least a plurality of distinctive search orientation blocks within a search orientation field may be initially generated for a search palm print image. A reference template may be obtained. A mated distinctive reference orientation block may be identified for each of the distinctive search orientation blocks. One or more regions of the search orientation field that include the distinctive search orientation blocks may be compared against one or more corresponding regions of the reference orientation field. An orientation similarity score between the search palm print image and the reference palm print image may be computed based on the comparison. A match may finally be determined if the computed orientation score satisfies a predetermined threshold value.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation application of, and claims priorityto, U.S. application Ser. No. 15/260,853, filed on Sep. 9, 2016.

FIELD

The present specification relates generally to biometricrecognition/identification systems.

BACKGROUND

Automatic biometric identification systems are commonly used in manycontexts to efficiently identify and verify the identity of individuals.For instance, automatic fingerprint identification systems often makesuch identification or verification based on distinctive characteristicsof fingerprints present on a target's finger. In such systems, thedistinctive and invariant characteristics are extracted from an imagethat includes classification type, core and delta, minutia points andcompared to those of a set of previously collected reference images.

SUMMARY

Traditional fingerprint identification techniques such as minutiaematching are often unable to be efficiently used with palm print images.For example, because the size of a palm print is much larger than afingerprint, a palm print includes many more pixels and a larger numberof minutiae than those of a fingerprint. Techniques used forfingerprints are often too slow to be effectively used in real-worldpalm print identification applications. In addition, in palm printidentification operations where one search palm print is comparedagainst multiple reference palm prints, the computational resourcesnecessary to perform all of the comparisons are often too high fortypical fingerprint identification systems.

Accordingly, one innovative aspect described throughout thisspecification includes systems and techniques capable of increasing thespeed and accuracy of matching two palm print images. The palm printmatching techniques include a two-step process that involving anorientation image matching procedure to quickly align two palm printimages, followed by minutiae matching procedure to determine whethergeometric features between the two palm prints correspond to determine asuccessful match.

The orientation matching procedure refers to the process of identifyingregions of a search orientation field that have similar ridge flowdirections to regions of a corresponding reference orientation field.This procedure can be used to identify regions of a reference palm printwith a similar orientation to that of the search palm print. Inaddition, techniques are described to iteratively perform theorientation matching procedure from a coarse-to-fine level (e.g., fromlow resolution to high resolution) in order to increase the overallspeed of the palm print matching techniques while also obtaining asufficiently accurate result by reducing the number of comparisons thatare necessary to obtain the sufficiently accurate match result.

As an example, at each individual stage, a list of mated block pairs areinitially identified between a search orientation field and a referenceorientation field using techniques described below. Only the top-rankedmated block pairs are then selected and subsequently compared in thenext matching stage at a finer level (e.g., higher resolutionorientation fields). In this regard, at each successive stage, thesearch orientation field and the reference orientation field arecompared at higher resolutions, but with a smaller number of mated blockpairs identified in the previous matching stage. Thus, the mostresource-intensive comparisons (e.g., finer level comparisons) are onlyperformed on the mated block pairs that are most likely to representmatching segments of a search palm print image and a reference palmprint image. This reduces the number of overall comparisons required toachieve a sufficiently accurate match result between the search palmprint image and the reference palm print image.

During the orientation matching procedure, the system initially computesrespective orientation similarity scores between a search palm printtemplate and a set of pre-generated reference palm print templates. Therespective orientation similarity scores are used to distinguish betweenreference palm prints that are likely to be mated with the search palmprint (e.g., reference palm print templates with a computed orientationsatisfying a predetermined threshold), and other reference palm printsthat are likely to be different from the search palm print. The systemthen computes transformation parameters for the mated referencetemplates based on an alignment procedure. The transformation parametersindicate geometric adjustments to the search palm print template tomaximize the number of corresponding minutiae between the transformedsearch palm print and a mated reference palm print template. By doingso, the time to perform for minutia matching in the second step isreduced.

During the minutiae matching procedure, the computed transformationparameters are used to generate a set of transformed search palm printtemplates that are aligned to each of the reference palm print templatesidentified in the orientation matching procedure. The system thenidentifies a set of mated minutiae within each transformed search palmprint image and its corresponding reference palm print image. Geometricfeatures associated with each of the mated minutiae are then compared inorder to compute a match score that reflects a similarity between thesearch palm print image and the corresponding reference palm printimage. The reference palm print images with the greatest similarityscores are then identified as potential match candidates for the searchpalm print image.

The techniques described above can be used to increase the matchingspeed and accuracy associated with a palm print matching operation. Forexample, during the orientation matching operation, because only asubset of reference templates that have an orientation similarity scorethat satisfy a threshold are selected and processed, the computationalresources associated with processing all reference palm print templates(including non-mated reference templates) is reduced. In addition,during the minutiae matching operation, only local minutiae identifiedwithin an overlapping region of the transformed search palm print imageand a corresponding reference palm print image are compared, furtherreducing the number of minutiae comparisons required to generate anaccurate match determination.

Implementations may include one or more of the following features. Forexample, a method for matching palm print images may be implemented byan automatic palm print identification system. The system may include aprocessor, a memory coupled to the processor, an interface to a palmprint scanning device, and a sensor associated with the palm printscanning device. The method may include: generating, for a search palmprint image, a search template that identifies at least a plurality ofdistinctive search orientation blocks within a search orientation fieldfor the search palm print image; obtaining a reference template thatidentifies a plurality of distinctive reference orientation blockswithin a reference orientation field for a reference palm print image;identifying, for each of the distinctive search orientation blocks, amated distinctive reference orientation block from among the pluralityof distinctive reference orientation blocks; comparing one or moreregions of the search orientation field that includes at least onedistinctive search orientation block with one or more correspondingregions of the reference orientation field that includes at least onedistinctive reference orientation block that correspond to the at leastone distinctive search orientation block; computing an orientationsimilarity score between the search palm print image and the referencepalm print image based at least on comparing the one or more regions ofthe search orientation field and the one or more regions of thereference orientation field; and determining that the computedorientation score satisfies a predetermined threshold value.

Other versions include corresponding systems, and computer programs,configured to perform the actions of the methods encoded on computerstorage devices.

One or more implementations may include the following optional features.For example, in some implementations, each distinctive searchorientation blocks indicates a respective ridge flow direction in aspatial location within the search orientation field corresponding toeach distinctive orientation block; and each distinctive referenceorientation blocks indicates a respective ridge flow direction in aspatial location within the reference orientation field corresponding toeach distinctive orientation block.

In some implementations, the one or more regions of the searchorientation field includes a plurality of neighboring search orientationfields for the at least one distinctive search orientation field; theone or more regions of the reference orientation field includes aplurality of neighboring reference orientation fields for the at leastone distinctive reference orientation field; and comparing the one ormore regions of the search orientation field and the one or more regionsof the reference orientation field includes: identifying, for each ofthe plurality of neighboring search orientation blocks, a respectivemated neighboring reference orientation block; comparing the respectiveridge flow directions indicated by each of the at least one distinctivesearch orientation blocks, and the respective ridge flow directionsindicated by each of the mated distinctive reference orientation blocks;and comparing the respective ridge flow directions indicated by each ofthe neighboring search orientation blocks, and the respective ridge flowdirections indicated by each of the mated neighboring referenceorientation blocks.

In some implementations, an orientation similarity score between thesearch palm print image and the reference palm print image includes:computing, for each of the one or regions of the search orientationfield, a respective orientation similarity score based at least on: (i)comparing the respective ridge flow direction indicated by each of theat least one distinctive search orientation blocks, and the respectiveridge flow directions indicated by each of the mated distinctivereference orientation blocks, and (ii) comparing the respective ridgeflow directions indicated by each of the neighboring search orientationblocks, and the respective ridge flow directions indicated by each ofthe mated neighboring reference orientation blocks; and selecting therespective orientation similarity score with the greatest value as theorientation similarity score between the search orientation field andthe reference orientation field.

In some implementations, comparing the one or more regions of the searchorientation field and the one or more regions of the referenceorientation field based on iteratively comparing one or more regions ofa particular down-sampled search orientation field and the one or moreregions of the reference orientation field, each iterative comparisonincluding: generating a down-sampled search orientation field that has aparticular resolution lower than the original resolution of the searchorientation field; computing a respective similarity score for each ofthe one or more regions of the down-sampled search orientation field anda corresponding mated region of the reference orientation field; andselecting a subset of top-ranked distinctive search orientation blocksbased at least on the computed values of the respective similarityscores for each of the one or more regions of the down-sampled searchorientation field and a corresponding mated region of the referenceorientation field.

In some implementations, the selected subset of top-ranked distinctivesearch orientation blocks of each successive iterative comparisonincludes a smaller number of distinctive search orientation blockscompared to the selected subset of top-ranked distinctive searchorientation blocks of the preceding iterative comparison.

In some implementations, the down-sampled search orientation fieldgenerated at each successive iterative comparison has a lower resolutioncompared to the resolution of the down-sampled search orientation fieldgenerated at the preceding iterative comparison.

In some implementations, the down-sampled search orientation field isgenerated during an enrollment stage of the search palm print image.

In some implementations, the obtained reference template furtheridentifies (i) a plurality of reference minutiae, and (ii) for eachreference minutia, a corresponding reference octant feature vector, andfurther includes: determining one or more transformation parametersbetween the search orientation field and the reference orientation fieldbased at least on comparing one or more regions of the searchorientation field and the one or more corresponding regions of thereference orientation field; aligning, using the one or moretransformation parameters, the search palm print image to a coordinatesystem associated with the reference palm print image, the alignedsearch palm print image including an overlapping region between thealigned palm print image and the reference palm print image; identifyinga plurality of search minutiae within the overlapping region of thealigned search palm print image; generating a search octant featurevector for each minutia within the identified plurality of searchminutiae; identifying, for each generated search octant feature vector,a mated reference octant feature vector from among the plurality ofreference octant feature vectors; comparing one or more featuresindicated by each search octant feature vector and one or morecorresponding features indicated by each mated reference octant featurevector; and computing a match similarity score between the search palmprint and the reference palm print based at least on comparing one ormore features indicated by each search octant feature vector and one ormore corresponding features indicated by each mated reference octantfeature vector.

In some implementations, the method includes identifying the pluralityof distinctive search orientation blocks, including: generating, for thesearch palm print image, a search orientation field representing aplurality of ridge flow directions associated with particular spatiallocations within the search palm print image; and identifying aplurality of search orientation blocks within the generated searchorientation field, each search orientation block indicated one ridgeflow direction associated with a particular spatial location within thesearch palm print image; determining, for each of the plurality ofsearch orientation blocks, a respective score representing anorientation difference between a particular search orientation block anda set of adjacent orientation blocks; and identifying a subset of theplurality of search orientation blocks that are determined to have arespective score that satisfies a predetermined threshold value.

In some implementations, the mated distinctive reference orientationblock is identified from among the plurality of distinctive referenceorientation blocks based at least on comparing local geometricattributes associated the one or more regions of the search orientationfield to local geometric attributes associated with one or more regionsof the reference orientation field.

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other potentialfeatures and advantages will become apparent from the description, thedrawings, and the claims.

Other implementations of these aspects include corresponding systems,apparatus and computer programs, configured to perform the actions ofthe methods, encoded on computer storage devices.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates a block diagram for an automatic fingerprintidentification system.

FIG. 1B illustrates a conceptual diagram of an exemplary palm printmatching procedure.

FIG. 2 illustrates a conceptual diagram of an example of a process forgenerating reference palm print templates.

FIG. 3 illustrates a conceptual diagram of a system for performing apalm print matching operation.

FIG. 4 illustrates a conceptual diagram of an example of a process forselecting distinctive orientation blocks for a search palm print image.

FIG. 5A illustrates a conceptual diagram of an example of a process thatcompares a set of mated search and reference orientation blocks.

FIG. 5B illustrates an iterative process that is used to verify theglobal consistency of the top-ranked orientation blocks.

FIG. 6 illustrates an example of a minutiae matching process.

In the drawings, like reference numbers represent corresponding partsthroughout.

DETAILED DESCRIPTION

In general, one innovative aspect described throughout thisspecification includes systems and techniques capable of increasing thespeed and accuracy of matching two palm print images. The palm printmatching techniques include a two-step process that involving anorientation image matching procedure to align two palm print images,followed by minutiae matching procedure to determine whether geometricfeatures between the two palm prints correspond to determine a successmatch.

During the orientation matching procedure, the system initially computesrespective orientation similarity score between a search palm printtemplate and a set of pre-generated reference palm print templates. Theorientation similarity score is used to distinguish between referencepalm prints that are likely to be mated with the search palm print(e.g., reference palm print templates with a computed orientationsatisfying a predetermined threshold), and other reference palm printsthat are likely to be different from the search palm prints. The systemthen computes transformation parameters for the mated referencetemplates based on the alignment procedure. The transformationparameters indicate geometric adjustments to the search palm printtemplate to maximize the number of corresponding minutiae between thetransformed search palm print and a mated reference palm print template.

During the minutiae matching procedure, the computed transformationparameters are used to generate a set of transformed search palm printtemplates that are aligned to each of the reference palm print templatesidentified in the orientation matching procedure. The system thenidentifies a set of mated minutiae within each transformed search palmprint image and its corresponding reference palm print image. Geometricfeatures associated with each of the mated minutiae are then compared inorder to compute a match score that reflects a similarity between thesearch palm print image and the corresponding reference palm printimage. The reference palm print images with the greatest similarityscores are then identified as potential match candidates for the searchpalm print image.

The following detailed description of the implementations of thedisclosure refers to the accompanying drawings. The same referencenumbers in different drawings may identify the same or similar elements.In addition, the following detailed description provides exemplaryimplementations of the concepts described within the claims, and shouldnot be read as to limit their scope.

As described, “alignment” or “registration” refers to a process ofpositioning two palm prints relative to one another to identify amaximum of number of corresponding minutiae between two palm printimages. For instance, a search palm print may be rotated and/ortransformed relative to the coordinate system of a reference palm printto identify mated minutiae between the reference palm print image andtransformed search palm print image.

An “overlapping region” of two palm prints refer to an intersecting areaof two palm prints after performing an alignment procedure. Theoverlapping region of two palm prints are used to identify matedminutiae between the two palm print images, and/or identifygeometrically consistent regions between the two palm prints.

An “orientation field” of a palm print image refers to an image thatrepresents a set of orientation directions corresponding to ridge flowdirections of a selected area of a palm print image. The dimensionalityof the orientation field can be scaled down by reducing the number oforientation directions included within the orientation field. Thedimensionality may be quantized into different scales (e.g., sixteen bysixteen pixels). The scaled orientation field can be referred to as an“orientation block,” which represents a set of individual ridge flowdirections.

A “minutiae” represent major features of a fingerprint, which are usedin comparisons of the reference fingerprint to the search fingerprint todetermine a fingerprint match. For example, common types of minutiae mayinclude, for example, a ridge ending, a ridge bifurcation, a shortridge, an island, a ridge enclosure, a spur, a crossover or bridge, adelta, or a core.

A “similarity score” is a measurement of the similarity of thefingerprint features (e.g., minutiae) between the search record and eachreference record, represented as a numerical value to degree ofsimilarity. For instance, in some implementations, the values of thesimilarity score may range from 0.0 to 1.0, where a higher magnituderepresents a greater degree of similarity between the search record andthe reference record.

System Architecture

FIG. 1A is a block diagram of an exemplary automatic palm printidentification system (APIS) 100. Briefly, the APIS 100 may include acomputing device including a memory device 110, a processor 115, apresentation interface 120, a user input interface 130, and acommunication interface 135. The APIS 100 may be configured tofacilitate and implement the methods described through thisspecification. In addition, the APIS 100 may incorporate any suitablecomputer architecture that enables operations of the system describedthroughout this specification.

The processor 115 may be operatively coupled to memory device 110 forexecuting instructions. In some implementations, executable instructionsare stored in the memory device 110. For instance, the APIS 100 may beconfigurable to perform one or more operations described by programmingthe processor 115. For example, the processor 115 may be programmed byencoding an operation as one or more executable instructions andproviding the executable instructions in the memory device 110. Theprocessor 115 may include one or more processing units, e.g., withoutlimitation, in a multi-core configuration.

The memory device 110 may be one or more devices that enable storage andretrieval of information such as executable instructions and/or otherdata. The memory device 110 may include one or more tangible,non-transitory computer-readable media, such as, without limitation,random access memory (RAM), dynamic random access memory (DRAM), staticrandom access memory (SRAM), a solid state disk, a hard disk, read-onlymemory (ROM), erasable programmable ROM (EPROM), electrically erasableprogrammable ROM (EEPROM), and/or non-volatile RAM (NVRAM) memory. Theabove memory types are exemplary only, and are thus not limiting as tothe types of memory usable for storage of a computer program.

The memory device 110 may be configured to store a variety of dataincluding, for example, matching algorithms, scoring algorithms, scoringthresholds, perturbation algorithms, fusion algorithms, virtual minutiaegeneration algorithms, minutiae overlap analysis algorithms, and/orvirtual minutiae analysis algorithms. In addition, the memory device 110may be configured to store any suitable data to facilitate the methodsdescribed throughout this specification.

The presentation interface 120 may be coupled to processor 115. Forinstance, the presentation interface 120 may present information, suchas a user interface showing data related to fingerprint matching, to auser 102. For example, the presentation interface 120 may include adisplay adapter (not shown) that may be coupled to a display device (notshown), such as a cathode ray tube (CRT), a liquid crystal display(LCD), an organic LED (OLED) display, and/or a hand-held device with adisplay. In some implementations, the presentation interface 120includes one or more display devices. In addition, or alternatively, thepresentation interface 120 may include an audio output device (notshown), e.g., an audio adapter and/or a speaker.

The user input interface 130 may be coupled to the processor 115 andreceives input from the user 102. The user input interface 130 mayinclude, for example, a keyboard, a pointing device, a mouse, a stylus,and/or a touch sensitive panel, e.g., a touch pad or a touch screen. Asingle component, such as a touch screen, may function as both a displaydevice of the presentation interface 120 and the user input interface130.

In some implementations, the user input interface 130 may represent afingerprint scanning device that is used to capture and recordfingerprints associated with a subject (e.g., a human individual) from aphysical scan of a finger, or alternately, from a scan of a latentprint. In addition, the user input interface 130 may be used to create aplurality of reference records.

A communication interface 135 may be coupled to the processor 115 andconfigured to be coupled in communication with one or more other devicessuch as, for example, another computing system (not shown), scanners,cameras, and other devices that may be used to provide biometricinformation such as fingerprints to the APIS 100. Such biometric systemsand devices may be used to scan previously captured fingerprints orother image data or to capture live fingerprints from subjects. Thecommunication interface 135 may include, for example, a wired networkadapter, a wireless network adapter, a mobile telecommunicationsadapter, a serial communication adapter, and/or a parallel communicationadapter. The communication interface 135 may receive data from and/ortransmit data to one or more remote devices. The communication interface135 may be also be web-enabled for remote communications, for example,with a remote desktop computer (not shown).

The presentation interface 120 and/or the communication interface 135may both be capable of providing information suitable for use with themethods described throughout this specification, e.g., to the user 102or to another device. In this regard, the presentation interface 120 andthe communication interface 135 may be used to as output devices. Inother instances, the user input interface 130 and the communicationinterface 135 may be capable of receiving information suitable for usewith the methods described throughout this specification, and may beused as input devices.

The processor 115 and/or the memory device 110 may also be operativelycoupled to the database 150. The database 150 may be anycomputer-operated hardware suitable for storing and/or retrieving data,such as, for example, pre-processed fingerprints, processedfingerprints, normalized fingerprints, extracted features, extracted andprocessed feature vectors such as octant feature vectors (OFVs),threshold values, virtual minutiae lists, minutiae lists, matchingalgorithms, scoring algorithms, scoring thresholds, perturbationalgorithms, fusion algorithms, virtual minutiae generation algorithms,minutiae overlap analysis algorithms, and virtual minutiae analysisalgorithms.

The database 150 may be integrated into the APIS 100. For example, theAPIS 100 may include one or more hard disk drives that represent thedatabase 150. In addition, for example, the database 150 may includemultiple storage units such as hard disks and/or solid state disks in aredundant array of inexpensive disks (RAID) configuration. In someinstances, the database 150 may include a storage area network (SAN), anetwork attached storage (NAS) system, and/or cloud-based storage.Alternatively, the database 150 may be external to the APIS 100 and maybe accessed by a storage interface (not shown). For instance, thedatabase 150 may be used to store various versions of reference recordsincluding associated minutiae, octant feature vectors (OFVs) andassociated data related to reference records.

Palm Print Matching

In general, palm print matching describes the process by which the APIS100 compares features of a search palm print to features of a set ofreference palm prints in order to make a match determination. In a 1:1comparison, a candidate search palm print image expected to beassociated with a known identity is compared against a correspondingstored reference palm print associated with the known identity in orderto determine whether the candidate search palm print image is actuallyassociated with the known identity. These techniques are often used fora palm print identify verification operation.

In a 1:N comparison, a candidate search palm print image with an unknownidentity is compared against a set of reference palm prints that areeach associated with different identities. These techniques are oftenused to identify a particular reference palm print that matches thecandidate search palm print image in order to identify the identityassociated with the candidate search palm print.

The systems and techniques described below are applicable to both 1:1and 1:N operations. However, for simplicity, the descriptions arefocused on 1:N operations where computational resources necessary toperform a palm print identification operation are important due to thenumber of comparisons performed by the APIS 100.

FIG. 1B is a block diagram of an exemplary palm print matching operation150. The APIS 100 initially receives a search palm print image 102. TheAPIS 100 then performs a feature extraction process for the search palmprint image 102. For instance, the APIS 100 generates an orientationfield 104 representing the ridge flow directions within the search palmprint image 102, extracts minutiae 110 within the search palm printimage 102, and generates octant feature vectors (OFVs) 112 for each ofthe extracted minutiae 110.

Referring initially to generation of the orientation field 104, the APIS100 evaluates relationships between the arrangements of individual ridgeflow directions within the orientation field 104 in order to identify aset of distinctive orientation blocks 106. More particular descriptionsrelated to distinctive orientation block calculation are provided belowwith respect to FIG. 4.

Referring now to minutiae extraction, after obtaining minutiae from thetransformed search palm print images and the corresponding referencepalm print images, the APIS 100 then obtains OFVs 112 for each of theminutiae. The OFVs encode relative geometric relationships between aparticular minutia and its eight nearest octant sector minutiae to forma rotation and translation invariant feature vector. Only the OFVs ofminutiae within the overlapping area are then compared against the OFVsof the corresponding local area in the overlapping area of the referencein order to calculate a match similarity score that represents thelikelihood that the corresponding reference palm print image associatedwith the reference template is a match for the search palm print image.More particular descriptions related to minutiae matching techniques aredescribed below with respect to FIGS. 6-11.

The APIS 100 then obtains a set of reference palm print templates fromstorage. Each reference palm print template is associated with aparticular reference palm print image and identifies (i) a set ofreference minutiae extracted from the particular reference palm printimage, (ii) a set of OFVs corresponding to the set of referenceminutiae, and (iii) a set of distinctive reference orientation blockswithin the orientation field of the particular reference palm printimage. More particular descriptions related to the generation andstorage of the reference palm print templates are provided below withrespect to FIG. 2.

The APIS 100 then compares the distinctive search orientation blocks 106a with the distinctive reference orientation blocks 106 b associatedwith each of the reference palm print templates. The comparisons arethen used to compute respective orientation similarity scores betweenthe search palm print template and each of the obtained reference palmprint templates. The values of the computed orientation similarityscores are then compared against a threshold value to identify a set ofreference templates 108 that have a computed orientation similarityscore that satisfies the threshold.

The comparisons of the distinctive search orientation blocks 106 a andthe distinctive reference orientation blocks 106 b are also used todetermine a set of transformation parameters that reflect adjustments tothe search palm print image, relative to each of the reference palmprint templates 108, in order to maximize the overlapped region betweenthe transformed search palm print image and a corresponding referencepalm print image.

The APIS 100 then uses the transformation parameters to perform alignthe search palm print image 102 according to the coordinate systems ofeach of the set of reference palm print templates 108 that areidentified to have an orientation similarity score that satisfies thepredetermined threshold. In this regard, the alignment procedure iscoarsely performed only for those reference templates that have a highlikelihood of representing a match candidate, reducing the number ofalignment procedures needed performed for the search palm print image102. After the alignment procedure, the APIS 100 generates set oftransformed search input images that are each aligned to a correspondingreference template within the set of reference templates 108. Moreparticular descriptions related to the calculation of orientationsimilarity scores, the computation of the transformation parameters, andthe alignment procedure using the computed transformation parameters areprovided below with respect to FIG. 5A.

The APIS 100 then identifies the overlapping regions between eachtransformed search palm print image and its corresponding referenceimage. As described above, the overlapping region represents an areawithin a particular transformed search palm print image and itscorresponding reference palm print image that shares certain localgeometric features (e.g., number of mated minutiae, common ridge flowpatterns, etc.). The APIS 100 then obtains search minutiae within theoverlapping region of each transformed search palm print image, andreference minutiae within the overlapping regions of the correspondingreference image.

In some implementations, the feature vectors included within the OFVsmay be described using feature vector that is represented byMf_(i)=(x_(i), y_(i), θ_(i)). The feature vector Mf_(i) includes aminutia location that is defined by coordinate geometry such as (x_(i),y_(i)), and a minutiae direction that is defined by the angle θ_(i)∈[0,2π]. In other examples, further minutiae characteristics such asquality, ridge frequency, and ridge curvature may also be used todescribe feature vector Mf_(i). The extracted feature vectors are usedto generate the OFVs for each of identified minutia within the searchand reference palm prints.

Reference Palm Print Generation

FIG. 2 illustrates a conceptual diagram of an example of a system 200that references reference palm print templates prior to a palm printmatching operation. The system 200 generally includes an orientationfield calculation module 210, an orientation field analysis module 220,an OFV generation module 230, and a template generation module 240. Insome instances, the system 200 may be a component within the APIS 100.Alternatively, in other instances, the system 200 may be a separatecomponent that is instead configured to exchange communications with theAPIS 100 and generated reference palm print templates in the database150.

The orientation field calculation module 210 initially receives a set ofreference palm print images 202. The set of reference palm print images202 can represent previously collected palm print images that aredetermined to be associated with a known identity. For example, the setof reference palm print images 202 can be palm print images that arecollected from users that enrolled into a particular biometric program.

The orientation field calculation module 210 then calculates anorientation field 204 for each reference image, and a set of extractedminutiae 206 during a feature extraction process. As described above,the orientation fields 204 represent the different ridge flow directionsthat are present within each of the reference palm print images 202. Theextracted minutiae represent distinctive points within each referencepalm print image that are associated with a minutia orientation and aminutia direction.

In some implementations, the orientation field calculation module 210may process the generated orientation fields 204 to reduce the file sizeof each orientation field. For example, the ridge flow directions ofeach orientation field can be quantized into smaller range values usingimage or data compression techniques to compress the orientation field.As an example, the orientations of four ridge flow directions can beaveraged together to generate a single average ridge flow direction tobe representative of the four individual ridge flow directions. Thisprocess can be iteratively performed for various regions within theorientation fields 204 to generate processed orientation fields thatinclude a smaller number of averaged ridge flow directions.

The orientation field analysis module 220 then identifies a list ofdistinctive orientation blocks 208 within each of the generatedorientation fields 204. The list of distinctive orientation blocks 208include orientation blocks that are associated with unique areas and/orlocations within each of the reference palm print images 202. Forexample, as described more particularly with respect to FIG. 4, aparticular orientation block may be selected as a distinctiveorientation block if it is determined to have a different orientationcompared to neighboring orientation blocks within a generate orientationfield. The list of distinctive orientation blocks 208 are used toindicate uniquely identifiable locations within each reference palmprint image that are not likely to be in any of the other palm printimages. Thus, during a palm print matching operation, the distinctiveorientation blocks of each of the reference palm print images can becompared against the distinctive orientation blocks of a search palmprint image to quickly identify potential match candidates withoutnecessarily comparing each region within the respective reference andsearch orientation fields.

The OFV generation module 230 then generates OFVs for each minutiaincluded within the list of extracted minutiae 206. For instance, asdescribed more particularly with respect to FIG. 2, the OFVs aregenerated based on comparing a minutia to its nearest neighboringminutiae within each octant, and generating feature vectors for eachoctant based on the comparison. The generated OFVs are rotation andtranslation invariant, and are useful in performing minutiae matchingoperations where an OFV for a search minutia is compared against an OFVfor a corresponding reference minutia to determine a match similarityscore.

The template generation module 240 finally generates a set of referencepalm print templates 214 corresponding to each of the reference palmprint images 202. Each reference palm print template 214 includes thelist of extracted minutiae 206, the list of distinctive orientationblocks 208, and the octant feature vectors 212 corresponding to the listof extracted minutiae 206. In this regard, the reference palm printtemplates 214 can be generated offline and stored in the database 150,and subsequently accessed by the APIS 100 during an online palm printmatching operation of a search palm print image.

Palm Print Matching Overview

FIG. 3 provides an overview of a system 300 for performing an onlinepalm print matching operation of a search palm print image 302. Thesystem 300 can be a software component of the APIS 100. The system 300includes a feature extraction module 310, an orientation image matchingmodule 320, and a minutia matching module 330. In the example depictedin FIG. 3, the system 300 performs a 1:N matching operation in which thesearch palm print image 102 is compared against a set of reference palmprint templates 214. As described above, once the features of the searchpalm print image 302 have been extracted, the matching operationgenerally includes orientation image matching, and minutiae matchingbetween a search palm print image template 312 and each of the referencepalm print templates 214.

Initially, the feature extraction module 310 generates an orientationfiled for the search palm print image 302, and then identifies a set ofdistinctive search orientation blocks using similar technique describedpreviously with respect to FIG. 2. The list of distinctive searchorientation blocks is then included within the search palm printtemplate 312 and provided to the orientation image matching module 320.

The orientation field matching module 320 then obtains the set ofreference palm print templates 214 that were generated and stored by thesystem 200. As described above, each obtained reference templateincludes a list of reference minutiae, OFVs corresponding to each of theminutiae, and a set of distinctive reference orientation blocks.

Upon receiving the search palm print template 312, the orientation fieldmatching module 320 then compares the each of the respective distinctivesearch and reference orientation fields to identify mated orientationblocks. A mated orientation block can refer to a pair of distinctivesearch and reference orientation blocks that are likely to refer tosimilar regions within a palm print image. For example, as describedabove, because each of the distinctive orientation blocks representuniquely identifiable information associated with a particular palmprint, the mated orientation blocks (e.g., orientation blocks that sharesimilar orientations and variances with neighboring orientation blocks)can be used to identify coarse match candidates within the set ofreference palm print templates 214.

The orientation field matching module 320 then compares attributesassociated with mated orientation blocks from the search palm printtemplate 312 with the corresponding reference palm print template. Forinstance, the orientation field matching module 320 may compare theorientation differences between neighboring orientation blocks of thedistinctive search and reference orientation blocks. The calculatedorientation differences for corresponding blocks within a local regioncan then be combined compute an orientation similarity score. Thisprocess can be iteratively performed for various coarse and fine levelsof the search palm print image 202.

The orientation field matching module 320 then compares each of thecomputed orientation similarity scores with a threshold value and thenselects a set of reference templates 214 a from among the reference palmprint templates 214 that have a computed orientation similarity scorethat satisfies the threshold. The selected reference palm printtemplates 214 a are associated with reference palm print images thathave a minimum-level orientation similarity with the search palm printimage 302 at or near locations associated with the distinctiveorientation blocks. The remaining reference templates within the set ofreference palm print templates 214 are determined by the orientationfield matching module 320 to be unlikely to represent a match candidatewith the search palm print image 302. This discrimination process isused to conserve computational resources by limiting the amount ofprocessing required during subsequent matching procedures (e.g., duringthe minutiae matching procedure) only to the reference palm printtemplates 214 a that are determined to have a high likelihood of being acandidate match.

The orientation field matching module 320 then computes a set oftransformation parameters 314 for the selected reference templates 314 athat have computed orientation similarity score that is greater than thepredetermined threshold. As described more particularly with respect toFIG. 5A, the transformation parameters 314 can include an optimalrotation and an optimal translation to maximize the overlapping regionbetween the search palm print image 302 and a corresponding referencepalm print image. The selected reference palm print images 214 a andtheir corresponding transformation parameters 314 are then transmittedto the minutiae matching module 330.

The minutiae matching module 330 then uses the transformation parameters314 to generate a set of transformed search palm print images that arealigned to each of the selected reference templates 214 a. The minutiaematching module 330 then identifies local minutiae within theoverlapping region of the transformed search palm print image, andgenerates an OFV for each local minutia.

The minutiae matching module 330 analyzes the local geometric featuresassociated with the transformed search palm print image and thecorresponding reference palm print image to identify all possible matedminutiae pairs that each include a set of search minutiae and a set ofreference minutiae that are determined to correspond to the set ofsearch minutiae. The OFVs of the individual minutiae within the matedminutiae pairs are then analyzed to compute match similarity scoresbetween the search palm print image 302 and each of the reference palmprint images associated with the selected reference palm print templates214 a.

The minutiae matching module 330 then generates a ranked list ofreference palm print images 322 based on the values of the respectivematch similarity scores computed for each of the selected reference palmprint templates 214 a. For instance, the minutiae matching module 330may select the reference palm print templates with the top N matchsimilarity scores and include these selected reference templates withthe list 332. This list identifies the reference palm print images thatare most likely to be a match for the search palm print image 302.

Although FIG. 3 provides a high-level overview of the entire palm printmatching process, detailed descriptions related to orientation fieldmatching procedure and the minutiae matching module are provided below.FIGS. 4, and 5 provide examples of concepts and techniques related toorientation field matching. FIGS. 6-11 provide examples of concepts andtechniques related to minutiae matching.

Orientation Field Matching

FIG. 4 illustrates a conceptual diagram of an example of a process forselecting distinctive orientation blocks for a search palm print image.As depicted, an orientation field window 402 may include multipleorientation blocks that include ridge flow directions 402 a-isurrounding a central orientation block 401. The orientation fieldwindow 402 may represent a portion of an orientation field generated fora palm print image (e.g., the search palm print image 302). Although theorientation field window 402 includes a three-by-three field window, inother examples, other window sizes can be used. For instance, in oneparticular implementation, a five-by-five field window can be used.

In order to identify the distinctive orientation blocks within agenerated orientation field, the orientation field matching module 320compares the orientation differences between the ridge flow direction ofthe central block 101 (or block B_(A)) to each of the ridge flowdirections 402 a-i of the blocks B₁-B₅. For instance, the orientationfield matching module 320 generates a table 410 that specifies anorientation difference between the block B_(A) and each individualneighboring block.

In the table 410, the orientation difference represents the absolutevalue of the angle of orientation of the central block (α) and the angleof orientation of each neighboring block (β_(i), where i represents theindex of the neighboring block). The table 410 also specifies a maximumorientation difference, which can be represented as the maximumorientation difference (θ_(max)) computed between a central block and anindividual orientation black within a particular orientation fieldwindow. In other instances, the maximum orientation difference mayinstead be computed by combining the individual measurements for theorientation difference (e.g., by computing an average orientationdifference within a particular orientation field window.

The orientation field matching module 320 then repeats the processdescribed above for each orientation block within the orientation fieldgenerated for a palm print image in order to generate the table 420. Thetable 420 specifies a ranked list of the maximum orientation differencesfor each orientation block within an orientation field. The orientationblocks with the greatest orientation differences are then selected asthe distinctive orientation blocks for the orientation field.

In some implementations, instead of iteratively computing the maximumorientation difference for each orientation block within an orientationfield, the orientation field matching module 320 may instead pre-processthe generated orientation field to reduce the number of orientationblocks included. For instance, as described above, the orientation fieldmatching module 320 may quantize the orientation field by combining anumber of ridge flow directions within specified locations into a singleridge flow direction that represents a composite ridge flow direction(e.g., replacing a quadrant of individual ridge flow directions with asingle orientation block with a composite ridge flow direction that iscomputed by combining the individual ridge flow directions).

FIGS. 5A-5B illustrates conceptual diagrams of examples of processes forcomparing mated orientation blocks including a search orientation blockand a reference orientation block. FIG. 5A depicts a process 500A forcomparing individual search and reference orientation field windows thateach include a distinctive orientation block, which is then repeated forall distinctive search orientation blocks to identify a set oftop-ranked mated pairs of search and reference orientation blocks. FIG.5B depicts an iterative process 500B that is used to verify the globalconsistency of the top-ranked orientation blocks identified afterperforming the process 500A, and then computing an overall orientationsimilarity score between the search and reference orientation fields.

Referring initially to FIG. 5A, the process 500A includes computingorientation differences for corresponding orientation blocks with acandidate mated orientation field window (510), and determining if thecomputed orientation differences exceed one or more threshold values(512 a, 512 b). If the candidate mated orientation field window isdetermined to be actually mated, then the process 500A includescomputing optimal transformation parameters based on each of theorientation field windows that are determined to be mated (514),applying the transformation parameters to regions surrounding thedistinctive orientation blocks (516) computing similarity scores betweentransformed search orientation blocks and corresponding referenceorientation blocks within the mated orientation block window (518), andselecting top-ranked orientation block pairs with the highest similarityscores (520).

In more detail, the orientation field matching module 320 initiallyidentifies a candidate mated orientation field window pair that includesa distinctive search orientation block B_(S) and a distinctive referenceorientation block B_(R). These two orientation blocks may be includedwithin local orientation field views 502 a and 502 b, respectively thatalso include neighboring orientation blocks (e.g., B_(S1)-B_(S8) for thesearch orientation block, and B_(R1)-B_(R8) for the referenceorientation block).

In order to determine whether the orientation blocks B_(S) and B_(R) areactually mated, the orientation filed matching module 320 determineswhether the orientation blocks B_(S) and B_(R) are geometricallyconsistent with one another. If the orientation blocks B_(S) and B_(R)are geometrically consistent, then each of corresponding neighboringorientation blocks within the orientation field windows 502 a and 502 bshould also be geometrically consistent with one another. As such, theorientation field matching module 320 compares the geometric attributesbetween B_(S) and B_(R), and each pair of neighboring blocks, as shownin table 504.

The orientation field matching module 320 initially identifies the firstneighboring block to compare based on the ridge flow directions of eachthe orientation fields B_(R) and B_(S). For instance, the orientationfield matching module 320 selects the particular neighboring orientationblock faces the direction of the ridge flow direction included withineach of the orientation blocks B_(R) and B_(S). The remainder of thecorresponding mated block pairs is sequentially identified in aclockwise fashion from the first identified neighboring block pair. Inthe example, B_(S1) and B_(R1) are identified as the first neighboringblock pair, and (B_(S2)-B_(S8), B_(R2)-B_(R8)) are then identified asthe remaining neighboring block pairs in a clockwise fashion from thepair B_(S2), B_(R2) to the pair B_(S8), B_(R8). This example representstwo orientation windows 502 a and 502 b that are aligned for simplicity,but in other instances, the first neighboring orientation field pair caninclude different neighboring orientation blocks if the orientationblocks B_(S) are B_(R) are rotated relative to one another (e.g., B_(S2)and B_(R6) identified as the first neighboring pair).

In general, the orientation field matching module 320 evaluates eachcandidate mated pair to verify whether the reference orientation blockwithin the reference orientation field corresponds to the searchorientation block specified by the candidate mated pair. Thiscorrespondence can be used to determine that a point within thereference palm print image that is associated with particular referenceorientation blocks that is aligned with the point within the search palmprint image that is associated with the particular search orientationblock. The orientation field matching module 320 than uses this point asreference to compute a set of transformation parameters (e.g., rotationand translation) to maximize an overlapping region between the referencepalm print image and the transformed search palm print image.

To determine whether the search orientation block B_(S) and thereference orientation block B_(R) are actually a mated pair, theorientation field matching module 320 compares the orientationdifferences (DO) between these two orientation blocks as well as theorientation differences between the corresponding neighboringorientation blocks (e.g., B_(S1) and B_(R1), B_(S2) and B_(R2), etc.)(510). The orientation field matching module 320 then generates a table504 that specifies the orientation difference between each comparisonbetween the search orientation field window 502 a and the referenceorientation field window 502 b. As described, above, the orientationdifference represents an angular difference, relative to a referenceaxis, between respective ridge flow directions included within the twoorientation blocks within a candidate mated pair.

The orientation field matching module 320 then compares the computeorientation differences specified by the table 504 to a set of thresholdvalues (512 a, 512 b). For instance, at step 512 a, the orientationfield matching module 320 determines whether the orientation differencebetween the search orientation block B_(S) and the reference orientationblock B_(R) exceeds a threshold value. If the orientation differenceexceeds the threshold value, then the orientation field matching module320 determines that the search and reference orientation blocks withinthe candidate mated pair are not actually mated. In this instance, thecandidate orientation matching module proceeds perform the techniquesdescribed above with the next candidate mated pair.

If the value of the orientation difference Dθ_(REF) is determined to beless than the threshold value, then the orientation field matchingmodule 320 proceeds to step 512 b. The orientation field matching module320 then compares each of the values of the orientation differencesDθ₁-Dθ₈ to a threshold. For instance, if either of the values of theorientation differences is greater than the threshold value, then theorientation field matching module 320 determines that the orientationblocks B_(S) and B_(R) are not actually matches, and proceeds to performthe techniques described above with the next candidate mated pair.

In some implementations, the thresholds in step 512 a and step 512 b mayrepresent the same threshold. In other implementations, the thresholdscan instead represent different values corresponding to the differencesin sensitivity used in each step. For example, a lower threshold may beused in 512 b if there is an increased likelihood that there may belarger orientation differences in the neighboring orientation blocks ofthe orientation field windows 502 a-502 b. In other instances, thethreshold values used in steps 512 a and 512 b may be adjusted based onthe particular location of the orientation field windows 502 a-502 bwithin the respective orientation fields.

If the orientation field matching module 320 determines that each of theorientation differences are below a threshold value, then theorientation filed matching module 320 determines that the candidatemated pair are actually mated, and then proceeds to repeat thetechniques described above for each of the other candidate mating pairs.As described more particularly with respect to FIG. 5B, this involvesiteratively executing the techniques described above for all possiblemated pairs.

Referring briefly to FIG. 5B, the orientation field matching module 320repeats steps 510, 512 a and 512 b of the process 500A for all of thepossible candidate mated orientation field windows. During the firstinstance of this process, all of the actually mated orientation fieldwindows are identified (e.g., the candidate mated orientation fieldwindows that are determined to include mated orientation blocks).

The orientation field matching module 320 then uses the geometricarrangement between each of the reference orientation blocks within thereference orientation field, relative to the geometric arrangementbetween each of the search orientation blocks within the searchorientation field, to generate a set of transformation parameters (506).For instance, as depicted in table 506, the transformation parametersinclude an optimal rotation and an optimal translation that maximizesthe area overlapping region specified by the distinctive orientationblocks within the mated pairs. The set of transformation parameters arestored and used in subsequently in the process 500B.

The orientation field matching module 320 then applies a transformationto regions surrounding the distinctive search orientation blocks usingthe transformation parameters (516). Although the transformationparameters are generated for the entire search orientation field,instead of applying the transformation to the entire search orientationfield, the orientation field matching module 320 instead applies thetransformation to regions surrounding the distinctive search orientationblocks within the search orientation field. For example, thetransformation may be applied to a sixteen-by-sixteen block windowsurrounding each distinctive search orientation block within the searchorientation field. In other examples, larger or smaller windows can alsobe used to optimize speed for performing the orientation matchingprocedure relative to the computational resource constraints forexecuting the procedure.

The orientation field matching module 320 then computes respectivesimilarity scores between the each of the transformed search orientationblocks and the corresponding reference orientation blocks within eachmated pair (518). For example, the orientation field matching module 320may compute similarity scores by combining the measured orientationdifferences specified in the table 510 and then comparing the combinedmeasurement to a predefined similarity threshold.

After computing the individual similarity scores for each mated pair ofreference and search orientation field windows, the orientation fieldmatching module 320 then ranks each mated pair by its similarity scorevalue, and selects the top-ranked mated pairs with the highestsimilarity score values (520). The search orientation block windowsincluded within top-ranked mated pairs represent regions within thesearch orientation field that are determined to be most similar tocorresponding regions within the reference orientation field. However,because the computed similarity scores reflect local consistency betweenthe search and reference orientation blocks (e.g., indicating similaritywith respect to regions specified by the orientation block windows 502 aand 502 b), the orientation field matching module 320 subsequentlyexecutes the process 500B depicted in FIG. 5B to ensure that thesetop-ranked regions are also globally consistent with one another. Asdescribed below, this includes an iterative process in which, at eachstage, the search orientation field is down-sampled to finer levels, andthe number of selected top-ranked regions are reduced to identify thebest-ranked search orientation block.

Referring now to FIG. 5B, an iterative process 500B is used by theorientation field matching module 320 to verify the global consistencyof top-ranked orientation blocks identified after performing the process500A. The output of the process 500A (e.g., the list of top-rankedsearch orientation blocks based on the respective similarity scorevalues of their corresponding mated pairs) are initially provided asinput the process 500B. In the example depicted in FIG. 5B, theorientation field matching module 320 initially identifies threetop-ranked mated pairs, which include three search orientation blocks(e.g., B_(S1), B_(S2), B_(S3)).

The orientation field matching module 320 then identifies all possiblecandidate mated pairs between search and reference orientation fields(560). In the example depicted, three search orientation blocks andthree reference orientation blocks are individually grouped to identifytwelve possible candidate mated orientation block pairs 552 a.

The orientation field matching module 320 then performs a set ofoperations related to ensuring that the top-ranked search orientationblocks are globally consistent with their corresponding referenceorientation blocks within the mated pairs within the respectiveorientation fields (562 a-d). For example, as described above, thesimilarity score calculation technique within process 500A ensures thattwo orientation blocks within a candidate mated pair are locallyconsistent within a particular orientation field window, but not for theentire orientation field.

To verify the global consistency of the top-ranked search orientationblocks with their corresponding reference orientation blocks, theorientation field matching module 320 initially obtains thetransformation parameters determined in step 514 of process 500A (562 a)and then down-samples the search orientation field using a scalingfactor (562 b). The down-sampled search orientation field has a samplingrate decreased by an integer factor compared to the search orientationfield at the original resolution. The orientation field matching module320 then applies the obtained transformation parameters to thedown-sampled search orientation field (562 c).

The orientation field matching module 320 then performs the techniquesdescribed previously with respect to process 500B to compute respectivesimilarity scores between the distinctive search orientation blockswithin the transformed down-sampled search orientation field and theircorresponding reference orientation blocks specified by their matedpair. This similarity score computation in step 562 d is performed inorder to identify the top-ranked search orientation blocks within thedown-sampled search orientation field at a coarser level. The identifiedsearch orientation blocks, their respective similarity scores, and theircorresponding rank are specified within table 554.

The orientation field matching module 320 also computes a similarityscore 554 a between the down-scaled search orientation filed and thereference orientation field. This similarity score represents arelationship between the number of candidate mated pairs that areidentified as actually being mated, and a number of candidate matedpairs that are identified as non-mated pairs. This similarity score 554a is used to reflect the impact of the down-sampling procedure on theidentification of top-ranked search orientation blocks. For example, ifa search orientation field is actually geometrically consistent with itscorresponding reference orientation field, then the similarity score 554a should reflect a minimal difference in the identification oftop-ranked search orientation blocks within the original searchorientation filed and the down-sampled search orientation field.

After performing a single iteration of the similarity score calculationprocess (e.g., steps 560 and 562 a-d), the orientation field matchingmodule 320 repeats this process with different down-scaling factors togenerate various down-scaled search orientation fields. In the exampledepicted in FIG. 5B, the orientation field matching module 320 repeatsthe steps 560 and 562 a-d over two iterations. However, in otherimplementations, the orientation matching module 320 may perform suchsteps over a greater number of iterations based on the geometricattributes search palm print image and/or computational requirementsassociated with the orientation matching procedure.

In the example, after computing the similarity score 554 a, theorientation field match module 320 then selects the top-ranked searchorientation blocks (562 e). The top-ranked search orientation blocks andtheir corresponding ranks are then identified in the table 554. Thetop-ranked search orientation blocks are identified based on the valueof the similarity score 554 a and the respective values of thesimilarity scores for each search orientation block that were previouslycalculated in FIG. 5A.

In some implementations, the similarity score 554 a is used to adjustthe respective values of the original similarity scores to account forthe differences in the original search orientation field and thedown-sampled search orientation field. In such implementations, thevalues of the similarity scores are ranked, and the top-ranked searchorientation blocks are selected from the ranked list.

Alternatively, in other implementations, instead of sorting therespective similarity scores associated with the search orientationblocks, the orientation field matching module 320 instead determineswhich particular search orientation block to include within the list oftop-ranked search orientation fields based on comparing the similarityscore 554 a to a predetermined threshold.

After selecting the top-ranked search orientation fields in step 562 e,the orientation field matching module 320 repeats the steps 560-562 e insteps 564-566 e. However, the difference between these two iterations isthat the number of top-ranked search orientation blocks is reduced witheach subsequent iteration (e.g., three top-ranked search orientationblocks at the beginning of step 560, and two top-ranked searchorientation blocks at the beginning of step 564). In this regard, withthe completion of each successive iteration, the orientation fieldmatching module 320 then narrows down the list of top-ranked searchorientation fields using different down-scaling factors (e.g., in steps562 b and 566 b for the first and second iterations, respectively).

After completing the multiple iterations, the orientation field matchingmodule 320 then selects the highest value similarity score from amongthe remaining list of top-ranked search orientation blocks as theorientation similarity between the search orientation field and thereference orientation field (568). In the example depicted in FIG. 5B,the similarity score associated with the mated pair includingorientation blocks B′_(S2) and B_(R1) identified in list 556 is selectedas the orientation similarity score between the search orientation fieldand the reference orientation field. The orientation field matchingmodule 320 then generates table 558, which identifies the orientationsimilarity score and the corresponding transformation parametersdetermined previously in the process 500A.

Minutiae Matching

A. Octant Feature Vector (OFV) Overview

The APIS 100 may compare the search and reference records based oninitially generating feature vectors associated with minutiae that areextracted from the search and reference records, respectively. Forinstance, as described throughout this specification, in someimplementations, octant feature vectors (OFVs) may be used to as featurevectors that define attributes of the extracted minutiae. However, inother implementations, other minutiae descriptors may be used.

OFVs encode geometric relationships between reference minutiae and thenearest neighboring minutiae to the reference minutiae in a particularsector (referred to as the “octant neighborhood”) of the octant. Eachsector of the octant used in an OFV spans 45 degrees of a fingerprintregion. The nearest neighboring minutiae may be assigned to one sectorof the octant based on their orientation difference. The geometricrelationship between a reference minutia and its nearest minutia in eachoctant sector may be described by relative features including, forexample, distance between the minutiae and the orientation differencebetween minutiae. The representation achieved by the use of an OFV isinvariant to transformation. In addition, this representation isinsensitive to a nonlinear distortion because the relative features areindependent from any transformation.

Pairs of reference minutiae and nearest neighboring minutiae may beidentified as “mated minutiae pairs.” The mated minutiae pairs in areference record and a search record may be identified by comparing therespective OFVs of minutiae extracted from the reference record and thesearch record. The transformation parameters may be estimated bycomparing attributes of the corresponding mated minutiae. For example,the transformation parameters may indicate the degree to which thesearch record has been transformed (e.g., perturbed or twisted) asrelative to a particular reference record. In other examples, thetransformation parameters may be applied to verify that, for aparticular pair of a reference record and a search record (a “potentialmatched fingerprint pair”), mated minutiae pairs exhibit correspondingdegrees of transformation. Based on the amount of corresponding matedminutiae pairs in each potential matched fingerprint pair, and theconsistency of the transformation, a similarity score may be assigned.In some implementations, the pair of potential matched fingerprint pairswith the highest similarity score may be determined as a candidatematched fingerprint pair.

The APIS 100 may calculate an OFV for each minutia that encodes thegeometric relationships between the reference minutia and its nearestminutiae in each sector of the octant. For instance, the APIS 100 maydefine eight octant sectors and assigns the nearest minutiae to onesector of the octant based on the location of each minutiae within thesectors. The geometric relationship between a reference minutia and itsnearest minutia in each octant sector is represented by the relativefeatures. For example, in some implementations, the OFV encodes thedistance, the orientation difference, and the ridge count differencebetween the reference feature and the nearest neighbor features. Becausethe minutia orientation can flexibly change up to 45° due to the octantsector approach, relative features are independent from anytransformation.

The APIS 100 may use the OFVs to determine the number of possiblecorresponding minutiae pairs. Specifically, the APIS 100 may evaluatethe similarity between two respective OFVs associated with the searchrecord and the file record. The APIS 100 may identify all possible localmatching areas of the compared fingerprints by comparing the OFVs. TheAPIS 100 may also an individual similarity score for each of the matedOFV pairs.

The APIS 100 may clusters all OFVs of the matched areas with similartransformation effects (e.g., rotation and transposition) into anassociated similar bin. Note that the precision of the clusters of thebins (e.g., the variance of the similar rotations within each bin) is aproxy for the precision of this phase. Automatic fingerprintidentification system 100 therefore uses bins with higher numbers ofmatched OFVs (e.g., clusters with the highest counts of OFVs) for thefirst phase global alignment.

The APIS 100 may use the location and angle of each selected bin as theparameters of a reference point (an “anchor point”) to perform a globalalignment procedure. More specifically, the APIS 100 may identify theglobal alignment based on the bins that include the greatest number ofthe global mated minutiae pairs, and the location and angle associatedwith each of those bins. Based on the number of global paired minutiaefound and the total of individual similarity scores calculated for thecorresponding OFVs within the bin or bins, the APIS 100 may identify thetransformations (e.g., the rotations of the features) with the bestalignment.

In a second phase, the APIS 100 performs a more precise pairing usingthe transformations with the best alignment to obtain a final set of theglobally aligned minutiae pairs. In this phase, automatic fingerprintidentification system 100 performs a pruning procedure to findgeometrically consistent minutiae pairs with tolerance of distortion foreach aligned minutiae set that factors in the local and globalgeometrical index consistency. By performing such alignment globally andlocally, automatic fingerprint identification system 100 determines thebest set of global aligned minutiae pairs. Automatic fingerprintidentification system 100 uses the associated mini-scores of the globalaligned pairs to calculate the global similarity score. Furthermore,automatic fingerprint identification system 100 factors in a set ofabsolute features of the minutiae, including the quality, ridgefrequency, and the curvatures in the computation of the final similarityscore.

B. Minutiae Matching Techniques

FIG. 6 illustrates an example of a minutiae matching process 600. Theprocess 600 may be performed by the AFIS 100 after performing theorientation field matching processes depicted in FIGS. 4, 5A-5B. In someinstances, the process 600 may be performed by the minutia matchingmodule 330.

Initially, a minutia extraction module 610 extracts a list of searchminutiae 604 from the search palm print image 602 as describedpreviously. The list of search minutiae 602 only includes minutiaewithin an overlapping region between a transformed search palm printimage and a corresponding reference palm print. In this regard, onlylocally matched minutiae from the transformed search palm prints areprocessed to improve the overall speed of the minutiae matching process.

An orientation generation module 620 then obtains transformationparameters for the search palm print image 602 and a correspondingreference palm print image from among the reference palm print images202 as described previously with respect to FIGS. 3 and 5A. Theorientation generation module 620 generates a set of transformed searchpalm print images for each of the reference palm print images using thetransformation parameters.

The orientation generation module 620 then obtains OFV for each specificsearch minutia included within the list of search minutiae 604 thatreside in an overlapping region within each transformed search palmprint image. Specifically, as described above, the orientationgeneration module 620 generates OFVs encoding the distance and theorientation difference between the search minutiae and the nearestneighbor in each of eight octant sectors. The generated OFVs are thenincluded within a list of search OFVs 606.

In the example depicted in FIG. 6, the OFV generation module 620generates OFVs for minutiae extracted from three transformed search palmprint images (e.g., S_(A), S_(B), and S_(C)). Each transformed searchpalm print image includes a set of minutiae, which the OFV generationmodule 620 generates OFVs for (e.g., MF_(A1)-MF_(AM) for S_(A),MF_(B1)-MF_(BM) for S_(B), and MF_(C1)-MF_(CM) for S_(C)). The list ofsearch OFVs 606 are then provided to an OFV comparison module 630.

The OFV comparison module 630 then compares the search OFVs for eachtransformed search palm print image to reference OFVs of thecorresponding reference palm print image. For instance, the OFVcomparison module 630 obtains the reference palm print templates 214from storage and then selects the reference OFVs included within thereference palm print templates 214.

In comparing the search OFVs of each transformed search palm print imageand the reference OFVs of the corresponding reference palm print imageassociated with a particular reference template, the OFV on module 630performs a coarse comparison to determine if the two OFVs have asimilarity above a predetermined threshold. For instance, the similarityof the search and reference OFVs can be determined based on theorientation difference and distance between the two minutiae associatedwith the OFVs. If the coarse comparison indicates that the OFVs have asimilarity above the predetermined threshold, then the OFV comparisonmodule 630 then computes a minutia similarity score that reflects a moreprecise similarity between a search minutia and a correspondingreference minutia. In this regard, the coarse comparison can be used todisregard two OFVs that are unlikely to include mated minutiae pairssuch that the OFV comparison module 630 performs precise minutiaematching only for OFV pairs that are likely to have mated minutiae. Thistechnique effectively reduces the speed associated with minutiaematching by reducing the number of matching procedures to be performed.

After comparing the search OFVs to the reference OFVs for eachtransformed search palm print image, the OFV comparison module 630 thengenerates a list of mated minutiae pairs 608 that specifies a minutiasimilarity score between a search minutia from the transformed searchpalm print image and its corresponding reference palm print image. Thevalues of the similarity scores are then sorted in descending order toidentify a list of possible mated minutiae pairs for each transformedsearch palm print image.

The identified mated minutiae pairs within the list 608 can then be usedto globally align the transformed search palm print image and itscorresponding reference palm print image and calculate an overallsimilarity score between the transformed search palm print image and thecorresponding reference palm print image. For example, in someinstances, the rotation angle between the search palm print image andthe reference palm print image determined in the orientation fieldmatching stage can be used to select a subset of the mated minutiaepairs, which are then used for minutiae pairing and global alignment ofthe transformed search palm print image and the reference palm printimage. In this example, the minutiae similarity score with the highestvalue can be selected as the overall similarity score between thetransformed search palm print image and the corresponding reference palmprint image.

The respective overall similarity scores between each transformed searchpalm print image and its corresponding reference palm print image arethen aggregated and sorted in a list. The values of respective overallsimilarity scores are used to identify the reference palm prints thatare most likely to represent match candidates for the input search palmprint image. For example, the AFIS 110 may use a predetermined thresholdfor the overall similarity score and determine that the reference palmprint images with an overall similarity score above the predeterminedthreshold are match candidates for the input search palm print image.The match results can then be provided for output.

Various implementations of the systems and methods described here can berealized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations of suchimplementations. These various implementations can includeimplementation in one or more computer programs that are executableand/or interpretable on a programmable system including at least oneprogrammable processor, which may be special or general purpose, coupledto receive data and instructions from, and to transmit data andinstructions to, a storage system, at least one input device, and atleast one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the terms “machine-readable medium”“computer-readable medium” refers to any computer program product,apparatus and/or device, e.g., magnetic discs, optical disks, memory,Programmable Logic Devices (PLDs), used to provide machine instructionsand/or data to a programmable processor, including a machine-readablemedium that receives machine instructions as a machine-readable signal.The term “machine-readable signal” refers to any signal used to providemachine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniquesdescribed here can be implemented on a computer having a display device,e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitorfor displaying information to the user and a keyboard and a pointingdevice, e.g., a mouse or a trackball by which the user can provide inputto the computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback, e.g., visual feedback,auditory feedback, or tactile feedback; and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in acomputing system that includes a back end component, e.g., as a dataserver, or that includes a middleware component, e.g., an applicationserver, or that includes a front end component, e.g., a client computerhaving a graphical user interface or a Web browser through which a usercan interact with an implementation of the systems and techniquesdescribed here, or any combination of such back end, middleware, orfront end components. The components of the system can be interconnectedby any form or medium of digital data communication, e.g., acommunication network. Examples of communication networks include alocal area network (“LAN”), a wide area network (“WAN”), and theInternet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

A number of embodiments have been described. Nevertheless, it will beunderstood that various modifications may be made without departing fromthe spirit and scope of the invention. In addition, the logic flowsdepicted in the figures do not require the particular order shown, orsequential order, to achieve desirable results. In addition, other stepsmay be provided, or steps may be eliminated, from the described flows,and other components may be added to, or removed from, the describedsystems. Accordingly, other embodiments are within the scope of thefollowing claims.

It should be understood that processor as used herein means one or moreprocessing units (e.g., in a multi-core configuration). The termprocessing unit, as used herein, refers to microprocessors,microcontrollers, reduced instruction set circuits (RISC), applicationspecific integrated circuits (ASIC), logic circuits, and any othercircuit or device capable of executing instructions to perform functionsdescribed herein.

It should be understood that references to memory mean one or moredevices operable to enable information such as processor-executableinstructions and/or other data to be stored and/or retrieved. Memory mayinclude one or more computer readable media, such as, withoutlimitation, hard disk storage, optical drive/disk storage, removabledisk storage, flash memory, non-volatile memory, ROM, EEPROM, randomaccess memory (RAM), and the like.

Additionally, it should be understood that communicatively coupledcomponents may be in communication through being integrated on the sameprinted circuit board (PCB), in communication through a bus, throughshared memory, through a wired or wireless data communication network,and/or other means of data communication. Additionally, it should beunderstood that data communication networks referred to herein may beimplemented using Transport Control Protocol/Internet Protocol (TCP/IP),User Datagram Protocol (UDP), or the like, and the underlyingconnections may comprise wired connections and corresponding protocols,for example, Institute of Electrical and Electronics Engineers (IEEE)802.3 and/or wireless connections and associated protocols, for example,an IEEE 802.11 protocol, an IEEE 802.15 protocol, and/or an IEEE 802.16protocol.

A technical effect of systems and methods described herein includes atleast one of: (a) increased accuracy in facial matching systems; (b)reduction of false accept rate (FAR) in facial matching; (c) increasedspeed of facial matching.

Although specific features of various implementations of the inventionmay be shown in some drawings and not in others, this is for convenienceonly. In accordance with the principles of the invention, any feature ofa drawing may be referenced and/or claimed in combination with anyfeature of any other drawing.

This written description uses examples to disclose the invention,including the best mode, and also to enable any person skilled in theart to practice the invention, including making and using any devices orsystems and performing any incorporated methods. The patentable scope ofthe invention is defined by the claims, and may include other examplesthat occur to those skilled in the art. Such other examples are intendedto be within the scope of the claims if they have structural elementsthat do not differ from the literal language of the claims, or if theyinclude equivalent structural elements with insubstantial differencesfrom the literal language of the claims.

As used herein, a processor may include any programmable systemincluding systems using micro-controllers, reduced instruction setcircuits (RISC), application specific integrated circuits (ASICs), logiccircuits, and any other circuit or processor capable of executing thefunctions described herein. The above examples are example only, and arethus not intended to limit in any way the definition and/or meaning ofthe term “processor.”

As used herein, the term “database” may refer to either a body of data,a relational database management system (RDBMS), or to both. As usedherein, a database may include any collection of data includinghierarchical databases, relational databases, flat file databases,object-relational databases, object oriented databases, and any otherstructured collection of records or data that is stored in a computersystem. The above examples are example only, and thus are not intendedto limit in any way the definition and/or meaning of the term database.Examples of RDBMS's include, but are not limited to including, Oracle®Database, MySQL, IBM® DB2, Microsoft® SQL Server, Sybase®, andPostgreSQL. However, any database may be used that enables the systemsand methods described herein. (Oracle is a registered trademark ofOracle Corporation, Redwood Shores, Calif.; IBM is a registeredtrademark of International Business Machines Corporation, Armonk, N.Y.;Microsoft is a registered trademark of Microsoft Corporation, Redmond,Wash.; and Sybase is a registered trademark of Sybase, Dublin, Calif.)

In some implementations, a computer program is provided, and the programis embodied on a computer readable medium. In an example implementation,the system is executed on a single computer system, without requiring aconnection to a sever computer. In a further implementation, the systemis being run in a Windows® environment (Windows is a registeredtrademark of Microsoft Corporation, Redmond, Wash.). In yet anotherimplementation, the system is run on a mainframe environment and a UNIX®server environment (UNIX is a registered trademark of X/Open CompanyLimited located in Reading, Berkshire, United Kingdom). The applicationis flexible and designed to run in various different environmentswithout compromising any major functionality. In some implementations,the system includes multiple components distributed among a plurality ofcomputing devices. One or more components may be in the form ofcomputer-executable instructions embodied in a computer-readable medium.

As used herein, an element or step recited in the singular and proceededwith the word “a” or “an” should be understood as not excluding pluralelements or steps, unless such exclusion is explicitly recited.Furthermore, references to “example implementation” or “someimplementations” of the present disclosure are not intended to beinterpreted as excluding the existence of additional implementationsthat also incorporate the recited features.

As used herein, the terms “software” and “firmware” are interchangeable,and include any computer program stored in memory for execution by aprocessor, including RAM memory, ROM memory, EPROM memory, EEPROMmemory, and non-volatile RAM (NVRAM) memory. The above memory types areexample only, and are thus not limiting as to the types of memory usablefor storage of a computer program.

A number of implementations have been described. Nevertheless, it willbe understood that various modifications may be made without departingfrom the spirit and scope of the invention. In addition, the logic flowsdepicted in the figures do not require the particular order shown, orsequential order, to achieve desirable results. In addition, other stepsmay be provided, or steps may be eliminated, from the described flows,and other components may be added to, or removed from, the describedsystems. Accordingly, other implementations are within the scope of thefollowing claims.

A number of implementations have been described. Nevertheless, it willbe understood that various modifications may be made without departingfrom the spirit and scope of the invention. In addition, the logic flowsdepicted in the figures do not require the particular order shown, orsequential order, to achieve desirable results. In addition, other stepsmay be provided, or steps may be eliminated, from the described flows,and other components may be added to, or removed from, the describedsystems. Accordingly, other implementations are within the scope of thefollowing claims.

It should be understood that processor as used herein means one or moreprocessing units (e.g., in a multi-core configuration). The termprocessing unit, as used herein, refers to microprocessors,microcontrollers, reduced instruction set circuits (RISC), applicationspecific integrated circuits (ASIC), logic circuits, and any othercircuit or device capable of executing instructions to perform functionsdescribed herein.

It should be understood that references to memory mean one or moredevices operable to enable information such as processor-executableinstructions and/or other data to be stored and/or retrieved. Memory mayinclude one or more computer readable media, such as, withoutlimitation, hard disk storage, optical drive/disk storage, removabledisk storage, flash memory, non-volatile memory, ROM, EEPROM, randomaccess memory (RAM), and the like.

Additionally, it should be understood that communicatively coupledcomponents may be in communication through being integrated on the sameprinted circuit board (PCB), in communication through a bus, throughshared memory, through a wired or wireless data communication network,and/or other means of data communication. Additionally, it should beunderstood that data communication networks referred to herein may beimplemented using Transport Control Protocol/Internet Protocol (TCP/IP),User Datagram Protocol (UDP), or the like, and the underlyingconnections may comprise wired connections and corresponding protocols,for example, Institute of Electrical and Electronics Engineers (IEEE)802.3 and/or wireless connections and associated protocols, for example,an IEEE 802.11 protocol, an IEEE 802.15 protocol, and/or an IEEE 802.16protocol.

A technical effect of systems and methods described herein includes atleast one of: (a) increased accuracy in facial matching systems; (b)reduction of false accept rate (FAR) in facial matching; (c) increasedspeed of facial matching.

Although specific features of various implementations of the inventionmay be shown in some drawings and not in others, this is for convenienceonly. In accordance with the principles of the invention, any feature ofa drawing may be referenced and/or claimed in combination with anyfeature of any other drawing.

This written description uses examples to disclose the invention,including the best mode, and also to enable any person skilled in theart to practice the invention, including making and using any devices orsystems and performing any incorporated methods. The patentable scope ofthe invention is defined by the claims, and may include other examplesthat occur to those skilled in the art. Such other examples are intendedto be within the scope of the claims if they have structural elementsthat do not differ from the literal language of the claims, or if theyinclude equivalent structural elements with insubstantial differencesfrom the literal language of the claims.

What is claimed is:
 1. A method performed by one or more computers, themethod comprising: obtaining, by the one or more computers, dataidentifying a first set of one or more regions of a search palm printimage that include one or more distinctive search orientation blockswithin a search orientation field generated for the search palm printimage, each distinctive search orientation block having an orientationdifference, relative to an adjacent search orientation block within thesearch orientation field, that satisfies a predetermined threshold;obtaining, by the one or more computers, data identifying a second setof one or more regions of a reference palm print image that include oneor more distinctive reference orientation blocks within a referenceorientation field generated for a reference palm print image, eachdistinctive reference orientation block having an orientationdifference, relative to an adjacent reference orientation block withinthe reference orientation field, that satisfies the predeterminedthreshold; computing, by the one or more computers, a similarity scorebetween the search palm print image and the reference palm print imagebased on the first set of one or more regions of the search palm printimage and the second set of one or more regions of the reference palmprint image; and providing, by the one or more computers, the similarityscore for output.
 2. The method of claim 1, wherein: each distinctivesearch orientation blocks indicates a respective ridge flow direction ina spatial location within the search orientation field corresponding toeach distinctive search orientation block; and each distinctivereference orientation blocks indicates a respective ridge flow directionin a spatial location within the reference orientation fieldcorresponding to each distinctive reference orientation block.
 3. Themethod of claim 1, wherein computing the similarity score between thesearch palm print image and the reference palm print image comprises:generating a down-sampled search orientation field that has a particularresolution lower than the original resolution of the search orientationfield; computing a respective similarity score for each of the one ormore regions of the down-sampled search orientation field and acorresponding mated region of the reference orientation field; andselecting a subset of top-ranked distinctive search orientation blocksbased at least on the computed values of the respective similarityscores for each of the one or more regions of the down-sampled searchorientation field and a corresponding mated region of the referenceorientation field.
 4. The method of claim 3, wherein the selected subsetof top-ranked distinctive search orientation blocks of each successiveiterative comparison includes a smaller number of distinctive searchorientation blocks compared to the selected subset of top-rankeddistinctive search orientation blocks of the preceding iterativecomparison.
 5. The method of claim 3, wherein the down-sampled searchorientation field generated at each successive iterative comparison hasa lower resolution compared to the resolution of the down-sampled searchorientation field generated at a preceding comparison.
 6. The method ofclaim 3, wherein the down-sampled search orientation field is generatedduring an enrollment stage of the search palm print image.
 7. The methodof claim 1, wherein: the obtained reference template identifies (i) aplurality of reference minutiae, and (ii) for each reference minutia, acorresponding reference octant feature vector; and the method furthercomprises: determining one or more transformation parameters between thesearch orientation field and the reference orientation field based atleast on comparing one or more regions of the search orientation fieldand the one or more corresponding regions of the reference orientationfield; aligning, using the one or more transformation parameters, thesearch palm print image to a coordinate system associated with thereference palm print image, the aligned search palm print imageincluding an overlapping region between the aligned palm print image andthe reference palm print image; identifying a plurality of searchminutiae within the overlapping region of the aligned search palm printimage; generating a search octant feature vector for each minutia withinthe identified plurality of search minutiae; identifying, for eachgenerated search octant feature vector, a mated reference octant featurevector from among the plurality of reference octant feature vectors;comparing one or more features indicated by each search octant featurevector and one or more corresponding features indicated by each matedreference octant feature vector; and computing a match similarity scorebetween the search palm print and the reference palm print based atleast on comparing one or more features indicated by each search octantfeature vector and one or more corresponding features indicated by eachmated reference octant feature vector.
 8. A system comprising: one ormore computers; and one or more storage devices storing instructionsthat, when executed by the one or more computers, cause the one or morecomputers to perform operations comprising: obtaining, by the one ormore computers, data identifying a first set of one or more regions of asearch palm print image that include one or more distinctive searchorientation blocks within a search orientation field generated for thesearch palm print image, each distinctive search orientation blockhaving an orientation difference, relative to an adjacent searchorientation block within the search orientation field, that satisfies apredetermined threshold; obtaining, by the one or more computers, dataidentifying a second set of one or more regions of a reference palmprint image that include one or more distinctive reference orientationblocks within a reference orientation field generated for a referencepalm print image, each distinctive reference orientation block having anorientation difference, relative to an adjacent reference orientationblock within the reference orientation field, that satisfies thepredetermined threshold; computing, by the one or more computers, asimilarity score between the search palm print image and the referencepalm print image based on the first set of one or more regions of thesearch palm print image and the second set of one or more regions of thereference palm print image; and providing, by the one or more computers,the similarity score for output.
 9. The system of claim 8, wherein: eachdistinctive search orientation blocks indicates a respective ridge flowdirection in a spatial location within the search orientation fieldcorresponding to each distinctive search orientation block; and eachdistinctive reference orientation blocks indicates a respective ridgeflow direction in a spatial location within the reference orientationfield corresponding to each distinctive reference orientation block. 10.The system of claim 8, wherein computing the similarity score betweenthe search palm print image and the reference palm print imagecomprises: generating a down-sampled search orientation field that has aparticular resolution lower than the original resolution of the searchorientation field; computing a respective similarity score for each ofthe one or more regions of the down-sampled search orientation field anda corresponding mated region of the reference orientation field; andselecting a subset of top-ranked distinctive search orientation blocksbased at least on the computed values of the respective similarityscores for each of the one or more regions of the down-sampled searchorientation field and a corresponding mated region of the referenceorientation field.
 11. The system of claim 10, wherein the selectedsubset of top-ranked distinctive search orientation blocks of eachsuccessive iterative comparison includes a smaller number of distinctivesearch orientation blocks compared to the selected subset of top-rankeddistinctive search orientation blocks of the preceding iterativecomparison.
 12. The system of claim 10, wherein the down-sampled searchorientation field generated at each successive iterative comparison hasa lower resolution compared to the resolution of the down-sampled searchorientation field generated at a preceding comparison.
 13. The system ofclaim 10, wherein the down-sampled search orientation field is generatedduring an enrollment stage of the search palm print image.
 14. Thesystem of claim 8, wherein: the obtained reference template identifies(i) a plurality of reference minutiae, and (ii) for each referenceminutia, a corresponding reference octant feature vector; and theoperations further comprise: determining one or more transformationparameters between the search orientation field and the referenceorientation field based at least on comparing one or more regions of thesearch orientation field and the one or more corresponding regions ofthe reference orientation field; aligning, using the one or moretransformation parameters, the search palm print image to a coordinatesystem associated with the reference palm print image, the alignedsearch palm print image including an overlapping region between thealigned palm print image and the reference palm print image; identifyinga plurality of search minutiae within the overlapping region of thealigned search palm print image; generating a search octant featurevector for each minutia within the identified plurality of searchminutiae; identifying, for each generated search octant feature vector,a mated reference octant feature vector from among the plurality ofreference octant feature vectors; comparing one or more featuresindicated by each search octant feature vector and one or morecorresponding features indicated by each mated reference octant featurevector; and computing a match similarity score between the search palmprint and the reference palm print based at least on comparing one ormore features indicated by each search octant feature vector and one ormore corresponding features indicated by each mated reference octantfeature vector.
 15. A non-transitory computer-readable storage deviceencoded with computer program instructions that, when executed by one ormore computers, cause the one or more computers to perform operationscomprising: obtaining, by the one or more computers, data identifying afirst set of one or more regions of a search palm print image thatinclude one or more distinctive search orientation blocks within asearch orientation field generated for the search palm print image, eachdistinctive search orientation block having an orientation difference,relative to an adjacent search orientation block within the searchorientation field, that satisfies a predetermined threshold; obtaining,by the one or more computers, data identifying a second set of one ormore regions of a reference palm print image that include one or moredistinctive reference orientation blocks within a reference orientationfield generated for a reference palm print image, each distinctivereference orientation block having an orientation difference, relativeto an adjacent reference orientation block within the referenceorientation field, that satisfies the predetermined threshold;computing, by the one or more computers, a similarity score between thesearch palm print image and the reference palm print image based on thefirst set of one or more regions of the search palm print image and thesecond set of one or more regions of the reference palm print image; andproviding, by the one or more computers, the similarity score foroutput.
 16. The device of claim 15, wherein: each distinctive searchorientation blocks indicates a respective ridge flow direction in aspatial location within the search orientation field corresponding toeach distinctive search orientation block; and each distinctivereference orientation blocks indicates a respective ridge flow directionin a spatial location within the reference orientation fieldcorresponding to each distinctive reference orientation block.
 17. Thedevice of claim 15, wherein computing the similarity score between thesearch palm print image and the reference palm print image comprises:generating a down-sampled search orientation field that has a particularresolution lower than the original resolution of the search orientationfield; computing a respective similarity score for each of the one ormore regions of the down-sampled search orientation field and acorresponding mated region of the reference orientation field; andselecting a subset of top-ranked distinctive search orientation blocksbased at least on the computed values of the respective similarityscores for each of the one or more regions of the down-sampled searchorientation field and a corresponding mated region of the referenceorientation field.
 18. The device of claim 17, wherein the selectedsubset of top-ranked distinctive search orientation blocks of eachsuccessive iterative comparison includes a smaller number of distinctivesearch orientation blocks compared to the selected subset of top-rankeddistinctive search orientation blocks of the preceding iterativecomparison.
 19. The device of claim 17, wherein the down-sampled searchorientation field generated at each successive iterative comparison hasa lower resolution compared to the resolution of the down-sampled searchorientation field generated at a preceding comparison.
 20. The device ofclaim 17, wherein the down-sampled search orientation field is generatedduring an enrollment stage of the search palm print image.